Unsupervised aspect level sentiment analysis using Ant Clustering and Self-organizing Maps

Chifu, Emil St.; Leţia, Tiberiu; Chifu, Viorica Rozina

doi:10.1109/sped.2015.7343075

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…The study concluded that NB method outperforms the other two approaches. Chifu et al (2015), proposed an unsupervised approach for aspect level sentiment analysis on product reviews. The proposed method used ant clustering algorithm to select the aspects of particular product.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Proposed pattern showed slightly better results compared to Hu & Liu (2004) approach. Chifu et al (2015) proposed an unsupervised method for aspect level opinion mining on product reviews. The proposed method used ant clustering algorithm to select the aspects of particular product.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…(Continued) Chifu et al (2015) Unsupervised method Proposed ant clustering algorithm to select the aspects of particular product. To classify the reviews, self-organizing map approach is proposed.…”

Section: Maharani Et Al (2015)mentioning

confidence: 99%

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Aspect-based classification of product reviews using Hadoop framework

Rodrigues¹,

Chiplunkar²,

Fernandes³

2020

Cogent Engineering

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The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated buy choice. A good number of customers share their conclusions about the product during these days. These reviews have an important role in customers' purchase-decision process. For a popular product there may be hundreds or thousands of reviews. This is difficult for a potential customer to go to each of them to make an educated choice on whether to buy the product or not. Also, this is difficult for manufacturer of the product to follow-up and to manage client opinions. In this scenario, the aspects-based sentimental analysis helps in analyzing the reviews and categorizing them into appropriate aspects. Aspect or feature refers to attributes or qualities of a product. The proposed work begins with collecting reviews from online shopping websites, identifying aspects and classifying opinion orientation of aspects with different sentiment analysis techniques using Hadoop framework. This paper proposes a new pattern-based method for aspect extraction and sentiment analysis which gives an accuracy in the ABOUT THE AUTHORS

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%